Abstract
In view of the EMU products have the huge amounts of data from different manufacturing information system. In the design, manufacture, operation and maintenance processes of the whole life cycle. Traditional tools and method cannot complete the data management tasks with huge data due to its defects. In this paper we introduce the k-means algorithm based on MapReduce parallel programming model, which can enhance the data processing and data mining efficiency. Experiments on the Hadoop cluster shows that the proposed algorithm is feasible, stable and efficient.
Keywords
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Supported by National High Technology Research and Development Program of China (2015AA043701).
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Liang, H., Feng, L., Chun, Z. (2017). Application of the Big Data Technology for Massive Data of the Whole Life Cycle of EMU. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_31
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DOI: https://doi.org/10.1007/978-3-319-49568-2_31
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